Enhancing Trajectory-Based Operations for UAVs through Hexagonal Grid Indexing: A Step towards 4D Integration of UTM and ATM

Aviation is expected to face a surge in the number of manned aircraft and drones in the coming years, making it necessary to integrate Unmanned Aircraft System Traffic Management (UTM) into Air Traffic Management (ATM) to ensure safe and efficient operations. This research proposes a novel hexagonal grid-based 4D trajectory representation framework for unmanned aerial vehicle (UAV) traffic management that overcomes the limitations of existing square/cubic trajectory representation methods. The proposed model employs a hierarchical indexing structure using hexagonal cells, enabling efficient ground based strategic conflict detection and conflict free 4D trajectory planning. Additionally, the use of Hexagonal Discrete Global Grid Systems provides a more accurate representation of UAV trajectories, improved sampling efficiency and higher angular resolution. The proposed approach can be used for predeparture conflict free 4D trajectory planning, reducing computational complexity and memory requirements while improving the accuracy of strategic trajectory conflict detection. The proposed framework can also be extended for air traffic flow management trajectory planning, Air Traffic Control (ATC) workload measurement, sector capacity estimation, dynamics airspace sectorization using hexagonal sectors and traffic density calculation, contributing to the development of an efficient UTM system, and facilitating the integration of UAVs into the national airspace system with AT

Categorization and Conversions for Indexing Methods of Discrete Global Grid Systems

Digital Earth frameworks provide a tool to receive, send and interact with large location-based datasets, organized usually according to Discrete Global Grid Systems (DGGS). In DGGS, an indexing method is used to assign a unique index to each cell of a global grid, and the datasets corresponding to these cells are retrieved or allocated using this unique index. There exist many methods to index cells of DGGS. Toward facility, interoperability and also defining a “standard” for DGGS, a conversion is needed to translate a dataset from one DGGS to another. In this paper, we first propose a categorization of indexing methods of DGGS and then define a general conversion method from one indexing to another. Several examples are presented to describe the method

Categorization and Conversions for Indexing Methods of Discrete Global Grid Systems

Digital Earth frameworks provide a tool to receive, send and interact with large location-based datasets, organized usually according to Discrete Global Grid Systems (DGGS). In DGGS, an indexing method is used to assign a unique index to each cell of a global grid, and the datasets corresponding to these cells are retrieved or allocated using this unique index. There exist many methods to index cells of DGGS. Toward facility, interoperability and also defining a “standard” for DGGS, a conversion is needed to translate a dataset from one DGGS to another. In this paper, we first propose a categorization of indexing methods of DGGS and then define a general conversion method from one indexing to another. Several examples are presented to describe the method

Towards Q-analysis Integration in Discrete Global Grid Systems: Methodology, Implications and Data Complexity

Spatial data is characterized by rich contextual information with multiple characteristics at each location. The interpretation of this multifaceted data is an integral part of current technological developments, data rich environments and data driven approaches for solving complex problems. While data availability, exploitation and complexity continue to grow, new technologies, tools and methods continue to evolve in order to meet these demands, including advancing analytical capabilities, as well as the explicit formalization of geographic knowledge. In spite of these developments Discrete Global Grid Systems (DGGS) were proposed as a new comprehensive approach for transforming scientific data of various sources, types and qualities into one integrated environment. The DGGS framework was developed as the global data model and standard for efficient storage, analysis and visualization of spatial information via a discrete hierarchy of equal area cells at various spatial resolutions. Each DGGS cell is the explicit representation of the Earth surface, which can store multiple data values and be conveniently recognized and identified within the hierarchy of the DGGS system. A detailed evaluation of some notable DGGS implementations in this research indicates great prospects and flexibility in performing essential data management operations, including spatial analysis and visualization. Yet they fall short in recognizing interactivity between system components and their visualization, nor providing advanced data friendly techniques. To address these limitations and promote further theoretical advancement of DGGS, this research suggests the use of Q-analysis theory as a way to utilize the potential of the hierarchical DGGS data model via the tools of simplicial complexes and algebraic topology. As a proof of concept and demonstration of Q-analysis feasibility, the method has been applied in a water quality and water health study, the interpretation of which has revealed much contextual information about the behaviour of the water network, the spread of pollution and chain affects. It is concluded that the use of Q-analysis indeed contributes to the further advancement and development of DGGS as a data rich framework for formalizing multilevel data systems and for the exploration of new data driven and data friendly approaches to close the gap between knowledge and data complexity